Volume 39 Issue 2
Apr.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(2): 101-108
ZUO Jingli, WANG Qiuping, CHEN Ju. A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation[J]. Journal of Transport Information and Safety, 2021, 39(2): 101-108. doi: 10.3963/j.jssn.1674-4861.2021.02.013
Citation: ZUO Jingli, WANG Qiuping, CHEN Ju. A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation[J]. Journal of Transport Information and Safety, 2021, 39(2): 101-108. doi: 10.3963/j.jssn.1674-4861.2021.02.013
shu

A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation

doi: 10.3963/j.jssn.1674-4861.2021.02.013

  • School of Civil Engineering, Xi'an University of Architecture & Technology, Xi'an 710055, China

  • Received Date: 2020-07-27
    Abstract
  • Since there is no direct connection between the GPS data and IC card data of some urban buses, it is difficult to correlate and obtain the passenger boarding data. The situation becomes more difficult when the two sets of data have irregular time deviations. The paper analyzes the fast matching data fusion of spatiotemporal characteristics, containing the following steps. Firstly, the bus timetable is obtained according to the bus GPS data and stop location matching. Then, the time similarity curve is drawn between the timetable and time-corrected IC card data through tra versal calculation. The corresponding relationship is found and verified by the curve of time-average deviation. Finally, the time correction value between the two systems is determined. The relevant three-day data is calculated on 195 buses in 5 routes in Xi'an city, where 191 vehicles have obvious identification characteristics. Besides, the algorithm is verified through 344 vehicles with known correspondences in 16 routes in Nanning City. The exact correspondence between 336 vehicles is obtained, with an average time corrected error of 16.5 s. The results show that the matching rate of the algorithm is 97.67%. For the widely existing bus GPS data and IC data belonging to different systems, it is difficult to judge the situation of bus stops by swiping the card. The proposed method expands the application scope of the original imperfect bus data and lays a foundation for analyzing individual micro travel behaviors in public transportation.

     

    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    杨东援, 段征宇. 大数据环境下城市交通分析技术[M]. 上海: 同济大学出版社, 2015.

    YANG Dongyuan, DUAN Zhengyu. Urban traffic analysis technology under big data environment[M]. Shanghai: Tongji University Press, 2015. (in Chinese)
    [2]
    PELLETIER M P, TREPANIER M, MORENCY C. Smart card data use in public transit: A literature review[J]. Transportation Research Part C: Emerging Technologies, 2011, 19(4): 557-568. doi: 10.1016/j.trc.2010.12.003
    [3]
    《中国公路学报》编辑部. 中国交通工程学术研究综述, 2016[J]. 中国公路学报, 2016, 29(6): 1-161. doi: 10.3969/j.issn.1001-7372.2016.06.001

    Editorial Department of China Journal of Highway and Transport. Review on China's traffic engineering research progress, 2016[J]. China Journal of Highway and Transport, 2016, 29 (6): 1-161. (in Chinese) doi: 10.3969/j.issn.1001-7372.2016.06.001
    [4]
    BAGCHI, WHITE P. Use of public transport smart card data for understanding travel behavior[C]. European Transport Conference 2003, Strasbourg, France: Association for European Transport, 2003.
    [5]
    陈学武, 戴霄, 陈茜. 公交IC卡信息采集、分析与应用研究[J]. 土木工程学报, 2004, 37(2): 105-110. doi: 10.3321/j.issn:1000-131X.2004.02.020

    CHEN Xuewu, DAI Xiao, CHEN Qian. Approach on the information collection, analysis and application of bus intelligent card[J]. China Civil Engineering Journal, 2004, 37(2): 105-110. (in Chinese) doi: 10.3321/j.issn:1000-131X.2004.02.020
    [6]
    ZHAO Jinhua, RAHBEE A, WILSON N H M, et al. Estimating a rail passenger trip origin destination matrix using automatic data collection systems[J]. Computer-Aided Civil & Infrastructure Engineering. 2007, 22(1): 376-387. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=25043140&site=ehost-live
    [7]
    BARRY J, FREIMER R, SLAVIN H. Use of entry-only automatic fare collection data to estimate linked transit trips in New York City[J]. Transportation Research Record, 2009 (2112): 53-61. http://www.researchgate.net/publication/238197099_Use_of_Entry-Only_Automatic_Fare_Collection_Data_to_Estimate_Linked_Transit_Trips_in_New_York_City
    [8]
    马晓磊, 刘从从, 刘剑锋, 等. 基于公交IC卡数据的上车站点推算研究[J]. 交通运输系统工程与信息, 2015, 15(4): 78-84. doi: 10.3969/j.issn.1009-6744.2015.04.012

    MA Xiaolei, LIU Congcong, LIU Jianfeng, et al. Boarding stop inference based on transit IC card data[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(4): 78-84. (in Chinese) doi: 10.3969/j.issn.1009-6744.2015.04.012
    [9]
    陈君, 杨东援. 基于智能调度数据的公交IC卡乘客上车站点判断方法[J]. 交通运输系统工程与信息, 2013, 13(1): 76-80. doi: 10.3969/j.issn.1009-6744.2013.01.013

    CHEN Jun, YANG Dongyuan. Identifying boarding stops of bus passengers with smart cards based on intelligent dispatching data[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(1): 76-80. (in Chinese) doi: 10.3969/j.issn.1009-6744.2013.01.013
    [10]
    秦政. 基于公交IC卡和GPS数据的乘客上下车站点研究[J]. 西部交通科技, 2017(8): 115-119. https://www.cnki.com.cn/Article/CJFDTOTAL-XBJT201708037.htm

    QIN Zheng. Research on passenger bus station based on bus IC card and GPS data[J]. Western China Communications Science & Technology. 2017(8): 115-119. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-XBJT201708037.htm
    [11]
    陈绍辉, 陈艳艳, 赖见辉. 基于GPS与IC卡数据的公交站点匹配方法[J]. 公路交通科技, 2012, 29(5): 102-108. doi: 10.3969/j.issn.1002-0268.2012.05.017

    CHEN Shaohui, CHEN Yanyan, LAI Jianhui. An approach on station ID and trade record match based on GPS and IC card data[J]. Journal of Highway and Transportation Research and Development, 2012, 29(5): 102-108. (in Chinese) doi: 10.3969/j.issn.1002-0268.2012.05.017
    [12]
    李海波, 陈学武, 陈峥嵘. 基于公交IC卡和AVL数据的客流OD推导方法[J]. 交通信息与安全, 2015, 33(6): 33-39+95. http://www.jtxa.net/tiasn/paper/editpaper.do?flag=abstract&PAPERID=2015-00293

    LI Haibo, CHEN Xuewu, CHEN Zhengrong. A method for estimating origin-destination matrix of public transit based on smart card and AVL data[J]. Journal of Transport Information and Safety, 2015, 33(6): 33-39+95. (in Chinese) http://www.jtxa.net/tiasn/paper/editpaper.do?flag=abstract&PAPERID=2015-00293
    [13]
    程晓明, 孙俊. 基于形态拟合的公交IC卡和公共汽车GPS时钟误差估计[C]. 2017年中国城市交通规划年会, 上海市, 中国: 中国城市规划学会城市交通规划学术委员会, 2017.

    CHENG Xiaoming, SUN Jun. Bus IC card and bus GPS clock error estimation based on morphological fitting[C]. 2017 China Urban Transportation Planning Annual Conference Proceedings, Shanghai, China: UPTS, 2017. (in Chinese)
    [14]
    TANG B, YIU M L, MOURATIDIS K, et al. Efficient motif discovery in spatial trajectories using discrete fréchet distance[C]. The 20th International Conference on Extending Database Technology, Venice, Italy: EDBT, 2017.
    [15]
    OKAMOTO K, BERNTORP K, CAIRANO S D. Similarity-based vehicle-motion prediction[C]. 2017 American Control Conference, New York, American: IEEE, 2017.
    [16]
    KIM J, MAHMASSANI H S. Spatial and temporal characterization of travel patterns in a traffic network using vehicle trajectories[J]. Transportation Research Procedia, 2015(9): 164-184. http://www.sciencedirect.com/science/article/pii/S2352146515001702
    [17]
    刘坤, 杨杰. 基于编辑距离的轨迹相似性度量[J]. 上海交通大学学报, 2009, 43(11): 1725-1729. doi: 10.3321/j.issn:1006-2467.2009.11.010

    LIU Kun, YANG Jie. Trajectory distance metric based on edit distance[J]. Journal of Shanghai Jiaotong University, 2009, 43 (11): 1725-1729. (in Chinese) doi: 10.3321/j.issn:1006-2467.2009.11.010
    [18]
    许佳捷, 郑凯, 池明旻, 等. 轨迹大数据: 数据、应用与技术现状[J]. 通信学报, 2015, 36(12): 97-105. https://www.cnki.com.cn/Article/CJFDTOTAL-TXXB201512010.htm

    XU Jiajie, ZHENG Kai, CHI Mingmin, et al. Trajectory big data: Data, applications and techniques[J]. Journal on Communications, 2016, 36(12): 97-105. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TXXB201512010.htm
    [19]
    杨健兵. Canopy和k-means聚类算法在公交IC卡数据分析中的应用研究[J]. 无线互联科技, 2019, 16(11): 125-128. https://www.cnki.com.cn/Article/CJFDTOTAL-WXHK201911058.htm

    YANG Jianbin. Research of canopy and k-means clustering algorithm in data analysis of the bus IC card[J]. Wireless Internet Technology, 2019, 16(11): 125-128. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-WXHK201911058.htm
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(8)  / Tables(4)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (384) PDF downloads(20) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return